DETAILED ACTION
This Office Action is in response to the communication filed on 6/11/2025.
Claims 1-20 are pending.
Claims 1-20 are rejected.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12353421. Although the claims at issue are not identical, they are not patentably distinct from each other because Patent No. 12353421 anticipated the claims recited in 19/234,457.
Instant application 19/234,457
Patent No. 12353421
Claims 1, 14 and 20: A data analytics system, comprising:
a data repository configured to store a plurality of pieces of data for multiple clients;
a metadata repository separate from the data repository and configured to store a plurality of pieces of metadata respectively corresponding to the plurality of pieces of data, each piece of metadata including technical metadata and usage metadata, the technical metadata being configured for creating a pipeline for providing the corresponding piece of data, and the usage metadata indicating whether the corresponding piece of data is sensitive;
a policy store configured to store a plurality of access control policies associated with the multiple clients; and
at least one processor configured to:
receive a request to provide one piece of data from among the plurality of pieces of data stored in the data repository from a user associated with a client;
verify that the user is authorized to access the piece of data based on an access control policy for the client, a role of the user, and the usage metadata included in a piece of metadata associated with the piece of data being requested;
create a pipeline using the technical metadata included in the piece of metadata associated with the piece of data being requested;
provide the data using the pipeline.
Claim 1:
a data repository configured to store a plurality of pieces of data for multiple clients;
a metadata repository separate from the data repository and configured to store a plurality of pieces of metadata;…
each piece of metadata among the plurality of pieces of metadata including technical metadata and usage metadata indicating whether the piece of data, which corresponds to the piece of metadata, is sensitive…
create a pipeline using the technical metadata…
a policy store configured to store a plurality of access control policies associated with the multiple clients;
receive a request to provide one piece of data from among the plurality of pieces of data stored in the data repository from a user associated with a client;
verify that the user with a specific role is authorized to access the piece of data based on the retrieved access control policy, the role of the user, and the retrieved piece of metadata associated with the piece of data being requested;
create a pipeline using the technical metadata included in the retrieved piece of metadata;
provide the data using the pipeline
Claims 2 and 15: The data analytics system of claim 1, wherein: the at least one processor is further configured to: provide a metadata management interface, the metadata management interface being configured to enable users to change the plurality of pieces of metadata in the metadata repository depending on roles of the users.
Claim 1: provide a metadata management interface, the metadata management interface configurable to enable users to change the plurality of pieces of metadata in the metadata repository depending on roles of the users.
Claim 3: The data analytics system of claim 2, wherein: each piece of metadata further includes quality metadata; and the metadata management interface is configured to display at least a portion of the quality metadata.Claim 4: The data analytics system of claim 2, wherein: the metadata management interface is configured to enable a user to tag a piece of data with a piece of metadata or manage rules associating the plurality of pieces of metadata with the plurality of pieces of data.Claim 16: The method of claim 15, wherein: each piece of metadata which is automatically generated for each piece of data further includes quality metadata; and the metadata management interface is configured to: display at least a portion of the quality metadata; and enable a user to tag a piece of data with a piece of metadata or manage rules associating the plurality of pieces of metadata with the plurality of pieces of data.
Claim 2: each piece of data further includes quality metadata; and the metadata management interface is configurable to display at least a portion of the quality metadata
Claim 3: the metadata management interface is configurable to enable a user to tag a piece of data with a piece of metadata or manage rules associating the plurality of pieces of metadata with the plurality of pieces of data.
Claim 2: each piece of data further includes quality metadata; and the metadata management interface is configurable to display at least a portion of the quality metadata
Claim 3: the metadata management interface is configurable to enable a user to tag a piece of data with a piece of metadata or manage rules associating the plurality of pieces of metadata with the plurality of pieces of data.
Claims 5 and 17: The data analytics system of claim 1, wherein: the at least one processor is further configured to: provide a policy management interface, the policy management interface configured to enable a user to specify policies for accessing the data; and creating or updating the policy in response to user interaction with the policy management interface and storing the policy in the policy store.
Claim 5: provide a policy management interface, the policy management interface configurable to enable a user associated with the client to specify policies for accessing the data; and the obtaining the policy comprises creating or updating the policy in response to user interaction with the policy management interface and storing the policy in the policy store.
Claims 6 and 18: The data analytics system of claim 1, wherein: the at least one processor is further configured to: provide a user monitoring interface, the user monitoring interface being configured to enable a user associated with the client to identify the piece of data as being accessed, and identify the user as accessing the piece of data.
Claim 6: provide a user monitoring interface, the user monitoring interface being configurable to enable a user associated with the client to identify the piece of data as being accessed, and identify the user as accessing the piece of data.
Claim 7: The data analytics system of claim 1, wherein: the plurality of pieces of data comprise structured and unstructured data; and the at least one processor is further configured to authorize access to the structured data and authorize access to the unstructured data.
Claim 7: the plurality of pieces of data comprise structured and unstructured data; and the at least one processor is further configured to authorize access to the structure data and authorize access to the unstructured data.
Claim 8: The data analytics system of claim 1, wherein the at least one processor is further configured to automatically generate the plurality of pieces of metadata by using a classifier or a pattern engine.
Claim 8: further configured to automatically generate the plurality of pieces of metadata by using a classifier or a pattern engine.
Claims 9 and 19: The data analytics system of claim 1, wherein the at least one processor is further configured to automatically generate the plurality of pieces of metadata by using a machine learning model.
Claim 1: automatically generate each of the plurality of pieces of metadata for each of the plurality of pieces of data in the data repository by using at least one machine learning model
Claim 10: The data analytics system of claim 1, wherein: the technical metadata includes lineage metadata specifying a mapping from a metric to the piece of data; the request to provide the piece of data includes instructions to create the pipeline to provide the piece of data; the instructions specify the metric; and the pipeline processes the piece of data according to the lineage metadata to create or update a value of the metric.
Claim 10: the technical data includes lineage metadata specifying a mapping from a metric to the piece of data; the request to provide the piece of data includes instructions to create the pipeline to provide the piece of data; the instructions specify the metric; and the pipeline processes the piece of data according to the lineage metadata to create or update a value of the metric.
Claim 11: The data analytics system of claim 1, wherein: the data repository includes an append-only data source and a data lake; and the pipeline creates data objects for storage in the data lake using the append-only data source and creates metadata for the data objects for storage in the metadata repository.
Claim 11: the data repository includes an append-only data source and a data lake; and the pipeline creates data objects for storage in the data lake using the append-only data source and creates metadata for the data objects for storage in the metadata repository.
Claim 12: The data analytics system of claim 1, wherein: the at least one processor is further configured to provide virtualized access to external data sources; the data comprises external data stored in the external data sources; and the at least one processor is further configured to automatically generate metadata for the external data.
Claim 12: the at least one processor is further configurable to provide virtualized access to external data sources; the data comprises external data stored in the external data sources; and the automatically generating the metadata comprises generating metadata for the external data.
Claim 13: The data analytics system of claim 1, wherein: the plurality of pieces of data are obtained from multiple sources; and the at least one processor is configured to: automatically generating the plurality of pieces of metadata for respectively corresponding ones of the plurality pieces of data obtained from the multiple sources; and integrating the plurality of pieces of metadata.
Claim 13: the plurality of pieces of data are obtained from multiple sources; and the automatically generating the plurality of pieces of metadata for the plurality of pieces of data in the data repository comprises: generating the plurality of pieces of metadata for respectively corresponding ones of the plurality pieces of data from the multiple sources; and integrating the plurality of pieces of metadata.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12353421. Although the claims at issue are not identical, they are not patentably distinct from each other because Patent No. 11829368 anticipated the claims recited in 19/234,457.
Instant application 19/234,457
Patent No. 11829368
Claims 1, 14 and 20: A data analytics system, comprising:
a data repository configured to store a plurality of pieces of data for multiple clients;
a metadata repository separate from the data repository and configured to store a plurality of pieces of metadata respectively corresponding to the plurality of pieces of data, each piece of metadata including technical metadata and usage metadata, the technical metadata being configured for creating a pipeline for providing the corresponding piece of data, and the usage metadata indicating whether the corresponding piece of data is sensitive;
a policy store configured to store a plurality of access control policies associated with the multiple clients; and
at least one processor configured to:
receive a request to provide one piece of data from among the plurality of pieces of data stored in the data repository from a user associated with a client;
verify that the user is authorized to access the piece of data based on an access control policy for the client, a role of the user, and the usage metadata included in a piece of metadata associated with the piece of data being requested;
create a pipeline using the technical metadata included in the piece of metadata associated with the piece of data being requested;
provide the data using the pipeline.
Claim 1: a data repository configured to store data for multiple clients;
a metadata repository separate from the data repository and configured to store metadata;…
the metadata including technical metadata and usage metadata…create the pipeline using the technical metadata…usage metadata indicating whether the data is sensitive…
a policy store separate from the data repository and the metadata repository, and configured to store a plurality of access control policies
receiving a request to provide the data from a user associated with the client
verify, by the access control system and based on the policy, that a role of the user and the usage metadata of the data satisfies the policy, and authorize, by the access control system, the request; and
create the pipeline using the technical metadata and
provide the data using the pipeline according to the usage metadata.
Claims 2 and 15: The data analytics system of claim 1, wherein: the at least one processor is further configured to: provide a metadata management interface, the metadata management interface being configured to enable users to change the plurality of pieces of metadata in the metadata repository depending on roles of the users.
Claim 2: provide a metadata management interface, the metadata management interface configurable to display at least a portion of the quality metadata for the data. Claim 3: the metadata management interface is configurable to enable a user to tag data with metadata or manage rules associating metadata with data.
Claim 3: The data analytics system of claim 2, wherein: each piece of metadata further includes quality metadata; and the metadata management interface is configured to display at least a portion of the quality metadata.
Claim 4: The data analytics system of claim 2, wherein: the metadata management interface is configured to enable a user to tag a piece of data with a piece of metadata or manage rules associating the plurality of pieces of metadata with the plurality of pieces of data.Claim 16: The method of claim 15, wherein: each piece of metadata which is automatically generated for each piece of data further includes quality metadata; and the metadata management interface is configured to: display at least a portion of the quality metadata; and enable a user to tag a piece of data with a piece of metadata or manage rules associating the plurality of pieces of metadata with the plurality of pieces of data.
Claim 2: the automatically generated metadata for the data further includes quality metadata; and provide a metadata management interface, the metadata management interface configurable to display at least a portion of the quality metadata for the data.
Claim 3: the metadata management interface is configurable to enable a user to tag data with metadata or manage rules associating metadata with data.
Claim 2: the automatically generated metadata for the data further includes quality metadata; and provide a metadata management interface, the metadata management interface configurable to display at least a portion of the quality metadata for the data.
Claim 3: the metadata management interface is configurable to enable a user to tag data with metadata or manage rules associating metadata with data.
Claims 5 and 17: The data analytics system of claim 1, wherein: the at least one processor is further configured to: provide a policy management interface, the policy management interface configured to enable a user to specify policies for accessing the data; and creating or updating the policy in response to user interaction with the policy management interface and storing the policy in the policy store.
Claim 4: provide a policy management interface, the policy management interface configurable to enable a user associated with the client to specify policies for accessing the data; and obtaining the policy comprising creating or updating the policy in response to user interaction with the policy management interface and storing the policy in the policy store.
Claims 6 and 18: The data analytics system of claim 1, wherein: the at least one processor is further configured to: provide a user monitoring interface, the user monitoring interface being configured to enable a user associated with the client to identify the piece of data as being accessed, and identify the user as accessing the piece of data.
Claim 5: provide a user monitoring interface, the user monitoring interface configurable to enable a user associated with the client to identify the data as being accessed, identify the user as accessing the data, and identify the policy as being the policy according to which the data is being accessed.
Claim 7: The data analytics system of claim 1, wherein: the plurality of pieces of data comprise structured and unstructured data; and the at least one processor is further configured to authorize access to the structured data and authorize access to the unstructured data.
Claim 6: the data comprises structured and unstructured data; the access control system comprises a query engine configured to authorize access to the structure data and a proxy service configured to authorize access to the unstructured data.
Claim 8: The data analytics system of claim 1, wherein the at least one processor is further configured to automatically generate the plurality of pieces of metadata by using a classifier or a pattern engine.
Claim 7: The data analytics system of claim 1, wherein: the metadata engine comprises one or more of a classifier or a pattern engine.
Claims 9 and 19: The data analytics system of claim 1, wherein the at least one processor is further configured to automatically generate the plurality of pieces of metadata by using a machine learning model.
Claim 1: automatically generate metadata… the metadata engine generating the metadata by using at least machine learning models
Claim 10: The data analytics system of claim 1, wherein: the technical metadata includes lineage metadata specifying a mapping from a metric to the piece of data; the request to provide the piece of data includes instructions to create the pipeline to provide the piece of data; the instructions specify the metric; and the pipeline processes the piece of data according to the lineage metadata to create or update a value of the metric.
Claim 9: the technical metadata includes lineage metadata specifying a mapping from a metric to the data; the instructions specify the metric; and the pipeline processes the data according to the lineage metadata to create or update a value of the metric.
Claims 1: request including instructions to create a pipeline to provide the data, the instructions independent of the source or structure of the data;
Claim 11: The data analytics system of claim 1, wherein: the data repository includes an append-only data source and a data lake; and the pipeline creates data objects for storage in the data lake using the append-only data source and creates metadata for the data objects for storage in the metadata repository.
Claim 10: the data repository includes an append-only data source and a data lake; and the pipeline creates data objects for storage in the data lake using the append-only data source and creates metadata for the data objects for storage in the metadata repository.
Claim 12: The data analytics system of claim 1, wherein: the at least one processor is further configured to provide virtualized access to external data sources; the data comprises external data stored in the external data sources; and the at least one processor is further configured to automatically generate metadata for the external data.
Claim 11: the data analytics system is further configurable to provide virtualized access to external data sources; the data comprises external data stored in the external data sources; and automatically generating the metadata comprises generating metadata for the external data.
Claim 13: The data analytics system of claim 1, wherein: the plurality of pieces of data are obtained from multiple sources; and the at least one processor is configured to: automatically generating the plurality of pieces of metadata for respectively corresponding ones of the plurality pieces of data obtained from the multiple sources; and integrating the plurality of pieces of metadata.
Claim 12: the data comprises data obtained from multiple sources; and automatically generating metadata for data in the data repository comprises: generating the metadata; and integrating the metadata.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-6, 8-10, 12-20 are rejected under 35 U.S.C. 103 as being unpatentable over Fan (U.S. 20210303584), in view of Shah (U.S. 20210011637).
Regarding claim 1,
Fan discloses: A data analytics system, comprising: (Fan [0014-0016, 0085-0088] teaches processing system which performs analytics)
a data repository configured to store a plurality of pieces of data for multiple clients; (Fan [0010] specified data sets from multiple sources... the client may provide the request by specifying the desired data; [0029] It should be noted that data from various data sources 125 may be filtered and transformed to achieve one or more data sets and/or subsets of data)
a metadata repository separate from the data repository and configured to store a plurality of pieces of metadata respectively corresponding to the plurality of pieces of data, (Fan [0061] teaches that the metadata and data can be stored on separate devices, and/or implement network elements as functions that are spread across several devices) each piece of metadata including technical metadata and usage metadata, the technical metadata being configured for creating a pipeline for providing the corresponding piece of data, (Fan [0039] teaches that each information model can comprise specification for creating a pipeline; [0040] describes a set on pipeline instructions; [0053] describes using those instructions for data retrieval i.e. making the pipeline; [0076 & Fig. 2] describe using information models comprising metadata which contains the data location).
a policy store configured to store a plurality of access control policies associated with the multiple clients; and (Fan [0052] Authorization module 112 may maintain records of the permissions for various ones of the client devices 188 and/or various users or user groups, the permissions of various data pipeline component types, the permissions for specific ones of the data pipeline components 127, data source(s) 125, and/or target(s) 129, and so forth. In one example, authorization module 112 may additionally include information regarding user preferences, limitations, exception handling procedures, etc.)
at least one processor configured to: (Fan [Abstract] a processing system including at least one processor)
receive a request to provide one piece of data from among the plurality of pieces of data stored in the data repository from a user associated with a client; (Fan [0010] the client may provide the request by specifying the desired data and the desired target(s), and the data pipeline controller may automatically generate an end-to-end plan to obtain and transmit the right data from the right source(s) to the right target(s); [0042] an example request 197 for delivery of data from one or more of the data sources 125 to one or more of the targets 129 may be processed by the data pipeline controller 110 as follows…)
verify that the user is authorized to access the piece of data based on an access control policy for the client, a role of the user, and the (Fan [0052] Once an information model is selected and finalized (e.g., approved for use and/or not objected to), the request interpreter and fulfillment module 111 may also verify that the client device 188 and/or a user thereof is authorized to create a data pipeline with regard to the data being requested, that the desired target(s) 129 are permitted to receive the requested data, that the client device 188 and/or a user thereof is permitted to utilize particular data pipeline components types that are indicated in the specification, and so forth. For instance, the request interpreter and fulfillment module 111 may submit the specification to authorization module 112 along with an identification of the one of the client devices 188 and/or an identification of a user thereof. Authorization module 112 may maintain records of the permissions for various ones of the client devices 188 and/or various users or user groups, the permissions of various data pipeline component types, the permissions for specific ones of the data pipeline components 127, data source(s) 125, and/or target(s) 129, and so forth)
create a pipeline using the technical metadata included in the piece of metadata associated with the piece of data being requested; (Fan [0052-0053] In one example, authorization module 112 may additionally include information regarding user preferences, limitations, exception handling procedures, etc. If the records associated with the user, the one of the client devices 188, the data pipeline component type(s), etc. are indicative that a data pipeline may be built or adapted to fulfill the request 197 in accordance with the selected information model, then the authorization module 112 may return an positive confirmation, or authorization, to the request interpreter and fulfillment module 111. In addition, upon receipt of a positive confirmation/authorization the request interpreter and fulfillment module 111 may submit the selected information model (e.g., along with parameters of the request 197), to the data pipeline management and assembly (DPMA) module 117).
provide the data using the pipeline. (Fan [0010] client requests may be fulfilled; [0081] At optional step 290, the processing system may transmit instructions to the plurality of data pipeline components in accordance with the plurality of data schemas to configure the plurality of data pipeline components into a data pipeline, where the data pipeline is for delivering the data set to the at least one destination)
While Fan discloses information which is used to determine authorization, Fan does not explicitly disclose that such data is metadata, for example Fan does not explicitly disclose: usage metadata indicating whether the corresponding piece of data is sensitive and usage metadata included in a piece of metadata associated with the piece of data being requested
However, in the same field of endeavor Shah discloses: usage metadata indicating whether the corresponding piece of data is sensitive and usage metadata included in a piece of metadata associated with the piece of data being requested (Shah [Fig. 2&4]; [0023] Decisions on whether to compress and/or encrypt files can be made on a file-by-file (or other data set size) basis. Furthermore, selective compression and selective encryption can be performed at a production site before starting any backup/replication process (i.e., before actually starting the data transfer from the production site to a remote site); [0053] Operation 414 determines if encryption is beneficial. In some embodiments, encryption is determined to be beneficial if there is a direct or indirect indication that the content in the file selected in operation 402 contains sensitive data (e.g., satisfies an encryption criteria) such as personal information, personally identifiable information, classified information, confidential information, private information, trade secret information, proprietary information, financial information, and/or other information that is deemed sensitive... Indications of sensitive data can be explicitly contained in metadata of a file and/or inferred from natural language processing (NLP) and/or other machine learning algorithms and techniques performed on the content of the file, the source of the file, and/or the type of file).
Fan and Shah are analogous art because they are from the same field of endeavor of information retrieval using metadata.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Fan and Shah before him or her, to modify the method of Fan to include the indications of sensitive data contained in metadata of Shah because it will the metadata used for pipeline creation of Fan to include metadata relation to determining user authorization to improve security.
The motivation for doing so would be [“the method can also result in improved security at least insofar as operation transmits encrypted files containing sensitive information.”] (Paragraphs 0024, 0028 and 0039 by Shah)].
Therefore, it would have been obvious to combine Fan and Shah to obtain the invention as specified in the instant claim.
Claim 14 recites limitations substantially similar in scope as claim 1 above, therefore, is also rejected under the same rationale set forth above. Additionally claim 14 discloses: A method performed by at least one processor in a data analytics system, the data analytics system comprising: (Fan [0087-0088] teaches processor executing the computer readable or software instructions relating to the above described method(s) can be perceived as a programmed processor or a specialized processor.
Claim 20 recites limitations substantially similar in scope as claim 1 above, therefore, is also rejected under the same rationale set forth above. Additionally claim 20 discloses: A non-transitory, computer-readable medium containing instructions that, when executed by at least one processor, cause a data analytics system to perform a method in a data analytics system, the data analytics system comprising: (Fan [0087-0088] teaches the present disclosure can be stored on a tangible or physical (broadly non-transitory) computer-readable storage device or medium)
Regarding claims 2 and 15,
Fan in view of Shah discloses all the limitations of claim 1,
Fan additionally discloses: The data analytics system of claim 1, wherein: the at least one processor is further configured to: (Fan [Abstract] a processing system including at least one processor)
provide a metadata management interface, (Fan [0051] the information model repository 114 may alternatively or additionally comprise an application programming interface (API) which may allow more direct access of the catalog of information models from the one of the client devices 188) the metadata management interface being configured to enable users to change the plurality of pieces of metadata in the metadata repository (Fan [0037], [0047-0051], [0068-0072], [0081-0084] the user may modify the information model and submit as a change to the information model, or may submit as a new information model) depending on roles of the users. (Fan [0052] Describes verifying users are authorized before proceeding with the metadata and user interaction processes which teaches allowing users to change the metadata if they are authorized users (authorized user being interpreted as a role)
Regarding claim 3,
Fan in view of Shah discloses all the limitations of claim 2,
Fan additionally discloses: The data analytics system of claim 2, wherein:
each piece of metadata further includes quality metadata; and (Fan [0012] the data pipeline controller may determine if intermediate nodes or data stores could be established to improve efficiency or other performance/quality; [0017] information models may have associated request templates which may be predefined (e.g., by a creator/administrator of an information model) and/or which may be learned over time as requests are matched to different information models, as feedback on the quality and correctness of the matching is provided by client request submitters; [0028] The data pipeline infrastructure 120 may include a plurality of data pipeline components 12; [0029] the data pipeline infrastructure 120 may also include one or more data sources 125...The data sources 125 may include network devices, e.g., routers, switches, multiplexers, firewalls, traffic shaping devices or systems, base stations, remote radio heads, baseband units, gateways, and so forth. The data from the data sources 125 may therefore comprise various types of network operational data, such as: channel quality information)
the metadata management interface is configured to display at least a portion of the quality metadata. (Fan [0084] In addition, although not expressly specified above, one or more steps of the method 200 may include a storing, displaying and/or outputting step as required for a particular application. In other words, any data, records, fields, and/or intermediate results discussed in the method can be stored, displayed and/or outputted to another device as required for a particular application... a module 305 for generating a data schema for a type of data pipeline component and storing an ontology and the data schema for the type of data pipeline component in a catalog of data pipeline component types... a display; [0051] the information model repository 114 may alternatively or additionally comprise an application programming interface (API) which may allow more direct access of the catalog of information models from the one of the client devices 188. In one example, user objects, information model objects, and data pipeline component type objects are all first class citizens in the architecture so any user could act on (view) any information model/template or data pipeline component type).
Regarding claim 4,
Fan in view of Shah discloses all the limitations of claim 2,
Fan additionally discloses: The data analytics system of claim 2, wherein:
the metadata management interface is configured to enable a user to tag a piece of data with a piece of metadata or manage rules associating the plurality of pieces of metadata with the plurality of pieces of data. (Fan [0084] In addition, although not expressly specified above, one or more steps of the method 200 may include a storing, displaying and/or outputting step as required for a particular application; [0042] To further illustrate the functions and features of data pipeline controller 110, an example request 197 for delivery of data from one or more of the data sources 125 to one or more of the targets 129 may be processed by the data pipeline controller 110 as follows. First, the request 197 may be crafted via a client device 188, which may specify a desired delivery of data from one or more of the data sources 125 to one or more of the targets 129. It should be noted that in one example, the request 197 may comprise a “trigger,” e.g., where the requesting client device 188 is an automated system. The request 197 may identify specific types of data, specific fields of data, specific sources or types of sources, geographic locations of sources or logical groupings of sources (e.g., all routers within a given network region, all devices in a subnet, all base stations in a selected state, wind speed information for a selected geographic area for a selected time period, all captured images or video in a selected area for a selected period of time, etc.). In one example, a user may generate the request 197 in accordance with a request template, such as in accordance with the example request template/specification and analysis/planning use pattern described above; [0048] a user of the client device 188 submitting the request 197 may be aware of a new type of data pipeline component that is desired to be included in the eventual data pipeline. As such, the user may modify the information model and submit as a change to the information model, or may submit as a new information model).
Regarding claims 5 and 17,
Fan in view of Shah discloses all the limitations of claim 1,
Fan additionally discloses: The data analytics system of claim 1, wherein:
the at least one processor is further configured to: (Fan [Abstract] a processing system including at least one processor)
provide a policy management interface, the policy management interface configured to enable a user to specify policies for accessing the data; and (Fan [0084] In addition, although not expressly specified above, one or more steps of the method 200 may include a storing, displaying and/or outputting step as required for a particular application; [0084] In addition, although not expressly specified above, one or more steps of the method 200 may include a storing, displaying and/or outputting step as required for a particular application. In other words, any data, records, fields, and/or intermediate results discussed in the method can be stored, displayed and/or outputted to another device as required for a particular application... a module 305 for generating a data schema for a type of data pipeline component and storing an ontology and the data schema for the type of data pipeline component in a catalog of data pipeline component types... a display; [0051] the information model repository 114 may alternatively or additionally comprise an application programming interface (API) which may allow more direct access of the catalog of information models from the one of the client devices 188. In one example, user objects, information model objects, and data pipeline component type objects are all first class citizens in the architecture so any user could act on (view) any information model/template or data pipeline component type; [0047] the request interpreter and fulfillment module 111 may scan the information models in the information model repository 114 to determine matching scores for different information models. However, in any case, the request interpreter and fulfillment module 111 may select one of the information models (e.g., the top matching information model) for use in establishing and/or reconfiguring a data pipeline to fulfill the request 197; [0048] For instance, a user of the client device 188 submitting the request 197 may be aware of a new type of data pipeline component that is desired to be included in the eventual data pipeline. As such, the user may modify the information model and submit as a change to the information model, or may submit as a new information model).
creating or updating the policy in response to user interaction with the policy management interface and storing the policy in the policy store. (Fan [0047] the request interpreter and fulfillment module 111 may scan the information models in the information model repository 114 to determine matching scores for different information models. However, in any case, the request interpreter and fulfillment module 111 may select one of the information models (e.g., the top matching information model) for use in establishing and/or reconfiguring a data pipeline to fulfill the request 197; [0048] For instance, a user of the client device 188 submitting the request 197 may be aware of a new type of data pipeline component that is desired to be included in the eventual data pipeline. As such, the user may modify the information model and submit as a change to the information model, or may submit as a new information model; [0051] It should be noted that new information models may be submitted in connection with a request fulfillment process, or may be submitted without connection to a particular request. For instance, a user may develop an information model for a new anticipated use case, without having a specific request for which a data pipeline is to be immediately built. In one example, a user, e.g., via one of the client devices 188 may browse the catalog of the information model repository 114 and may utilize any existing information models as a template for a new information model. As illustrated in FIG. 1, the interactions of data pipeline controller 110 and one of the client devices 188 for generating and/or submitting a new information model may be via information model updater/generator module 113; [0062] an additional module may be provided to store previously processed requests as request templates, to store request templates and the associations between request templates and information models, to provide the request templates to clients, to obtain feedback on the matching of requests and/or request templates to information models (and/or the resulting data pipelines), to learn and update associations between request templates and information models, and so forth).
Regarding claims 6 and 18,
Fan in view of Shah discloses all the limitations of claim 1,
Fan additionally discloses: The data analytics system of claim 1, wherein:
the at least one processor is further configured to: (Fan [Abstract] a processing system including at least one processor)
provide a user monitoring interface, (Fan [0067] At optional step 225, the processing system may present the template to an operator, e.g., via an endpoint device of an operator. The presentation may include options for the operator to modify, and/or to approve or deny the adoption of the template as the first data schema; [0084] In addition, although not expressly specified above, one or more steps of the method 200 may include a storing, displaying and/or outputting step as required for a particular application) the user monitoring interface being configured to enable a user associated with the client to identify the piece of data as being accessed, and identify the user as accessing the piece of data. (Fan [0013] a data pipeline controller of the present disclosure is aware of each data pipeline that is in existence, and knows each data pipeline's history; [0052] The request interpreter and fulfillment module 111 may submit the specification to authorization module 112 along with an identification of the one of the client devices 188 and/or an identification of a user thereof. Authorization module 112 may maintain records of the permissions for various ones of the client devices 188 and/or various users or user groups, the permissions of various data pipeline component types, the permissions for specific ones of the data pipeline components 127, data source(s) 125, and/or target(s) 129, and so forth; [0084] In addition, although not expressly specified above, one or more steps of the method 200 may include a storing, displaying and/or outputting step as required for a particular application)).
Regarding claim 8,
Fan in view of Shah discloses all the limitations of claim 1,
Fan additionally discloses: The data analytics system of claim 1, wherein the at least one processor is further configured to automatically generate the plurality of pieces of metadata by using a classifier or a pattern engine. (Fan [0017] teaches that the information model may learn over time; [0034&0036] teach defining classes associated with data; [0038] teaches the informational model may comprise metadata).
Regarding claims 9 and 19,
Fan in view of Shah discloses all the limitations of claim 1,
While Fan discloses using machine learning to automatically generating schema (metadata) as indicated above.
Fan does not specifically disclose: The data analytics system of claim 1, wherein the at least one processor is further configured to automatically generate the plurality of pieces of metadata by using a machine learning model.
However, in the same field of endeavor Shah discloses: The data analytics system of claim 1, wherein the at least one processor is further configured to automatically generate the plurality of pieces of metadata by using a machine learning model. (Shah [Fig. 2&4]; [0023] Decisions on whether to compress and/or encrypt files can be made on a file-by-file (or other data set size) basis. Furthermore, selective compression and selective encryption can be performed at a production site before starting any backup/replication process (i.e., before actually starting the data transfer from the production site to a remote site); [0053] Operation 414 determines if encryption is beneficial. In some embodiments, encryption is determined to be beneficial if there is a direct or indirect indication that the content in the file selected in operation 402 contains sensitive data (e.g., satisfies an encryption criteria) such as personal information, personally identifiable information, classified information, confidential information, private information, trade secret information, proprietary information, financial information, and/or other information that is deemed sensitive... Indications of sensitive data can be explicitly contained in metadata of a file and/or inferred from natural language processing (NLP) and/or other machine learning algorithms and techniques performed on the content of the file, the source of the file, and/or the type of file).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify with Shah for similar reasons as cited in claim 1.
Regarding claim 10,
Fan in view of Shah discloses all the limitations of claim 1,
Fan additionally discloses: The data analytics system of claim 1, wherein:
the technical metadata includes lineage metadata specifying a mapping from a metric to the piece of data; (Fan [0047, 0053 & 0076] describe information models which comprise metadata and are being used to provide parameters to map the locations of data; [0046&0047] describes information models and how they map metrics to data)
the request to provide the piece of data includes instructions to create the pipeline to provide the piece of data; (Fan [0038] describes that information models, which are used to establish pipelines for providing data, may be part of the request)
the instructions specify the metric; and (Fan [0038] teaches instructions for specific metrics used for mapping/identification of data)
the pipeline processes the piece of data according to the lineage metadata to create or update a value of the metric. (Fan [0037-0038 & 0039] teach updating the information model based on metrics).
Regarding claim 12,
Fan in view of Shah discloses all the limitations of claim 1,
Fan additionally discloses: The data analytics system of claim 1, wherein:
the at least one processor is further configured to provide virtualized access to external data sources; (Fan [0011 & 0028 & 0086] describe virtualization platforms for performing the functions of the application which include [0038] pipelines for accessing/utilizing data)
the data comprises external data stored in the external data sources; and (Fan [0011 & 0028] describe cloud storage which is external storage)
the at least one processor is further configured to automatically generate metadata for the external data. (Fan [0035] describes automatically generating metadata and storing it in the repository; [0011] indicates this data can be stored in the cloud).
Regarding claim 13,
Fan in view of Shah discloses all the limitations of claim 1,
Fan additionally discloses: The data analytics system of claim 1, wherein:
the plurality of pieces of data are obtained from multiple sources; and (Fan [0010] teaches the data can be obtained from multiple sources).
the at least one processor is configured to: (Fan [Abstract] a processing system including at least one processor)
automatically generating the plurality of pieces of metadata for respectively corresponding ones of the plurality pieces of data obtained from the multiple sources; and (Fan [0035] describes automatically generating metadata which corresponds to obtained pieces of data from one or more sources)
integrating the plurality of pieces of metadata. (Fan [0017 & 0038] describe that metadata, which is used as part of the information model, can be predefined, predefined meaning standardized as indicated in paragraph 0068).
Regarding claim 16,
Fan in view of Shah discloses all the limitations of claim 15,
Fan additionally discloses: The method of claim 15, wherein:
each piece of metadata which is automatically generated for each piece of data further includes quality metadata; and (Fan [0012] the data pipeline controller may determine if intermediate nodes or data stores could be established to improve efficiency or other performance/quality; [0017] information models may have associated request templates which may be predefined (e.g., by a creator/administrator of an information model) and/or which may be learned over time as requests are matched to different information models, as feedback on the quality and correctness of the matching is provided by client request submitters; [0028] The data pipeline infrastructure 120 may include a plurality of data pipeline components 12; [0029] the data pipeline infrastructure 120 may also include one or more data sources 125...The data sources 125 may include network devices, e.g., routers, switches, multiplexers, firewalls, traffic shaping devices or systems, base stations, remote radio heads, baseband units, gateways, and so forth. The data from the data sources 125 may therefore comprise various types of network operational data, such as: channel quality information)
the metadata management interface is configured to: (Fan [0037], [0047-0051], [0068-0072], [0081-0084] the user may modify the information model and submit as a change to the information model, or may submit as a new information model)
display at least a portion of the quality metadata; and (Fan [0084] In addition, although not expressly specified above, one or more steps of the method 200 may include a storing, displaying and/or outputting step as required for a particular application. In other words, any data, records, fields, and/or intermediate results discussed in the method can be stored, displayed and/or outputted to another device as required for a particular application... a module 305 for generating a data schema for a type of data pipeline component and storing an ontology and the data schema for the type of data pipeline component in a catalog of data pipeline component types... a display; [0051] the information model repository 114 may alternatively or additionally comprise an application programming interface (API) which may allow more direct access of the catalog of information models from the one of the client devices 188. In one example, user objects, information model objects, and data pipeline component type objects are all first class citizens in the architecture so any user could act on (view) any information model/template or data pipeline component type).
enable a user to tag a piece of data with a piece of metadata or manage rules associating the plurality of pieces of metadata with the plurality of pieces of data. (Fan [0084] In addition, although not expressly specified above, one or more steps of the method 200 may include a storing, displaying and/or outputting step as required for a particular application; [0042] To further illustrate the functions and features of data pipeline controller 110, an example request 197 for delivery of data from one or more of the data sources 125 to one or more of the targets 129 may be processed by the data pipeline controller 110 as follows. First, the request 197 may be crafted via a client device 188, which may specify a desired delivery of data from one or more of the data sources 125 to one or more of the targets 129. It should be noted that in one example, the request 197 may comprise a “trigger,” e.g., where the requesting client device 188 is an automated system. The request 197 may identify specific types of data, specific fields of data, specific sources or types of sources, geographic locations of sources or logical groupings of sources (e.g., all routers within a given network region, all devices in a subnet, all base stations in a selected state, wind speed information for a selected geographic area for a selected time period, all captured images or video in a selected area for a selected period of time, etc.). In one example, a user may generate the request 197 in accordance with a request template, such as in accordance with the example request template/specification and analysis/planning use pattern described above; [0048] a user of the client device 188 submitting the request 197 may be aware of a new type of data pipeline component that is desired to be included in the eventual data pipeline. As such, the user may modify the information model and submit as a change to the information model, or may submit as a new information model).
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Fan (U.S. 20210303584), in view of Shah (U.S. 20210011637) in further view of Karinta (U.S. 20160188898).
Regarding claim 7,
Fan in view of Shah discloses all the limitations of claim 1,
Fan additionally discloses: The data analytics system of claim 1, wherein:
Fan does not explicitly disclose: the plurality of pieces of data comprise structured and unstructured data; and the at least one processor is further configured to authorize access to the structured data and authorize access to the unstructured data However, in the same field of endeavor Karinta teaches: the plurality of pieces of data comprise structured and unstructured data; and the at least one processor is further configured to authorize access to the structured data and authorize access to the unstructured data. (Karinta [0006] teaches role based access control for data stored on data containers which applies to structured or unstructured data).
Fan, Shah and Karinta are analogous art because they are from the same field of endeavor of data access control.
Before the effective filing date of the claimed inventions, it would have been obvious to one of ordinary skill in the art, having the teachings of Fan, Shah and Karinta before him or her, to modify the method of Fan to include the role based access of Karinta because it will provide different parameters for different data according to what roles should and should not have access to what data.
The motivation for doing so would be [“assign a role to the user from among a plurality of roles that determines an access type and based on the role the user is granted the access type to a stored object and a storage service associated with the storage object is enabled by the management device in order to provide appropriate protections to data while allowing appropriate management of the data” (Paragraph 0009 & 0224 by Karinta)].
Therefore, it would have been obvious to combine Fan and Shah with Karinta to obtain the invention as specified in the instant claim.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Fan (U.S. 20210303584), in view of Shah (U.S. 20210011637) in further view of Fower (U.S. 20200327978).
Regarding claim 11,
Fan in view of Shah discloses all the limitations of claim 1,
AAA additionally discloses: The data analytics system of claim 1, wherein:
the data repository includes an (Fan [0028-0029] teaches data repositories including cloud data storage including “private” cloud storage)
the pipeline creates data objects for storage in the data lake using the (Fan [0051] teaches creation of data objects and storage of data objects in repositories; [0028] indicates this storage can be in the cloud)
Fan in view of Shah does not specifically disclose “append-only” or imputable storage.
However, in the same field of endeavor Fower teaches append-only data source: (Fower [0083] teaches immutable data storage with data retrieval features).
Fan, Shah and Fower are analogous art because they are from the same field of endeavor of data management.
Before the effective filing date of the claimed inventions, it would have been obvious to one of ordinary skill in the art, having the teachings of Fan, Shah and Fower before him or her, to modify the method of Fan to include the role based access of Fower because it will provide immutable data storage which can be later retrieved.
The motivation for doing so would be to provide [“fully HIPPA compliant with permanent safe storage across a decentralized platform—a giant leap forward from current cloud-based PACS solutions.” (Paragraph 0009 & 0224 by Fower)].
Therefore, it would have been obvious to combine Fan and Shah with Fower to obtain the invention as specified in the instant claim.
Conclusion
The prior art made of record but not relied upon and considered pertinent to applicant's
disclosure.
Harada 2019-9-26 (US 20210303721) teaches: Data management in a database system to process data requests and generate metadata for processing the requests.
Huang 2011-6-10 (US 20130073573) teaches: Methods for processing database query’s and establishing pipelines for processing the query requests.
Searls 2017-2-14 (US 10685033) teaches: Methods for fulfilling requests for data from databased and building pipelines for processing such requests.
Klausberger 2006-5-3 (EP 1732019) teaches: Search engines for searching within home networks shall be optimized. Therefore, it is suggested to dynamically adapt a weight of an individually metadata of a data unit of the database (1) by analyzing an external database. For example, web page information (6) is used to individually influence the ranking of the search results.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to THOMAS A CARNES whose telephone number is (571)272-4378. The examiner can normally be reached Monday-Friday.
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THOMAS A. CARNES
Examiner
Art Unit 2436
/THOMAS A CARNES/Examiner, Art Unit 2436
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